Jerzy Stefanowski
Geciteerd door
Geciteerd door
Ensemble learning for data stream analysis: A survey
B Krawczyk, LL Minku, J Gama, J Stefanowski, M Woźniak
Information Fusion 37, 132-156, 2017
Incomplete information tables and rough classification
J Stefanowski, A Tsoukias
Computational intelligence 17 (3), 545-566, 2001
SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
JA Sáez, J Luengo, J Stefanowski, F Herrera
Information Sciences 291, 184-203, 2015
Reacting to different types of concept drift: The accuracy updated ensemble algorithm
D Brzezinski, J Stefanowski
IEEE Transactions on Neural Networks and Learning Systems 25 (1), 81-94, 2013
On the extension of rough sets under incomplete information
J Stefanowski, A Tsoukias
International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular …, 1999
Lingo: Search results clustering algorithm based on singular value decomposition
S Osiński, J Stefanowski, D Weiss
Intelligent information processing and web mining, 359-368, 2004
On rough set based approaches to induction of decision rules
J Stefanowski
Rough sets in knowledge discovery 1 (1), 500-529, 1998
Open challenges for data stream mining research
G Krempl, I Žliobaite, D Brzeziński, E Hüllermeier, M Last, V Lemaire, ...
ACM SIGKDD explorations newsletter 16 (1), 1-10, 2014
Local neighbourhood extension of SMOTE for mining imbalanced data
T Maciejewski, J Stefanowski
2011 IEEE symposium on computational intelligence and data mining (CIDM …, 2011
Learning from imbalanced data in presence of noisy and borderline examples
K Napierała, J Stefanowski, S Wilk
International conference on rough sets and current trends in computing, 158-167, 2010
Types of minority class examples and their influence on learning classifiers from imbalanced data
K Napierala, J Stefanowski
Journal of Intelligent Information Systems 46 (3), 563-597, 2016
ROSE-software implementation of the rough set theory
B Predki, R Słowiński, J Stefanowski, R Susmaga, S Wilk
International Conference on Rough Sets and Current Trends in Computing, 605-608, 1998
Selective pre-processing of imbalanced data for improving classification performance
J Stefanowski, S Wilk
International Conference on Data Warehousing and Knowledge Discovery, 283-292, 2008
Variable consistency model of dominance-based rough sets approach
S Greco, B Matarazzo, R Slowinski, J Stefanowski
International Conference on Rough Sets and Current Trends in Computing, 170-181, 2000
An algorithm for induction of decision rules consistent with the dominance principle
S Greco, B Matarazzo, R Slowinski, J Stefanowski
International conference on rough sets and current trends in computing, 304-313, 2000
Neighbourhood sampling in bagging for imbalanced data
J Błaszczyński, J Stefanowski
Neurocomputing 150, 529-542, 2015
Rough classification in incomplete information systems
R SLOWIŃSKI, J Stefanowski
Models and Methods in Multiple Criteria Decision Making, 1347-1357, 1989
Accuracy updated ensemble for data streams with concept drift
D Brzeziński, J Stefanowski
International conference on hybrid artificial intelligence systems, 155-163, 2011
Combining block-based and online methods in learning ensembles from concept drifting data streams
D Brzezinski, J Stefanowski
Information Sciences 265, 50-67, 2014
Three discretization methods for rule induction
JW Grzymala‐Busse, J Stefanowski
International Journal of Intelligent Systems 16 (1), 29-38, 2001
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Artikelen 1–20